International Journal of Computer Applications (0975 – 8887)

Volume 91 – No.16, April 2014

38

A Survey on Real Time Object Detection, Tracking and

Recognition in Image Processing

C. Hemalatha S. Muruganand R. Maheswaran Department of Electronics and

Instrumentation Department of Electronics and

Instrumentation Department of Electronics and

Instrumentation Bharathiar University Bharathiar University Bharathiar University

Coimbatore Coimbatore Coimbatore India India India

ABSTRACT Object detection, tracking and recognition in real time is a

very essential task in computer vision. There are lots of

research work have been done in this area. Yet it needs to be

accuracy in recognizing object. The most objective of this

review is to present an overview of the approaches used and

also the challenges involved. In this paper we concentrate on

different object detection methods, tracking and recognition

methods are discussed.

Keywords object detection, object tracking, object recognition, survey

1. INTRODUCTION In computer vision, Real Time object recognition is one

among the foremost research area. The object recognition

systems are going to include detection and tracking the object

is in motion and its position relevant to scene. Lots of research

work has been done in this emerging field. It is very useful for

the quality control and inspection task in industry. Object

recognition is very successful one in controlled environment.

But it is terribly tough in uncontrolled atmosphere. Several

methods have been proposed to recognize objects in real time.

Section two describes the review of real time object detection

and tracking. The over review of object recognition is

described in section three and conclusion is well delineating

in section four.

2. REVIEW OF REAL TIME OBJECT

DETECTION AND TRACKING Moving object detection is a first step for object classification

and object recognition. Chih-Hsien Hsia et al [1] proposed

Efficient modified directional lifting-based discrete wavelet

transform (MDLDWT) for moving object detection. The

proposed method detects multiple moving objects very

perfectly even though the object shape issues in the low

resolution configuration is vague. The merit of MDLDWT is

reduced the cast and protect the fine shape information in low

resolution.

Victoria Yanulevskaya et al [2] proposed an object-based

visual attention theory for the task of salient object detection.

They assume proto-object being a unit of attention and argue

that notion of an object should be taken into account while

assessing object saliency. In this paper two types of object

saliency is used and they combined together detects the object

in the image. The two objects salient are center surround

saliency and integrated saliency. Centre surround saliency

detects an object different from its surrounding object how

many rare details present in the objects are detected by integrated saliency.

The contribution of Pranab Kumar Dhar et al [3] proposed an

efficient moving object detection method is used for video

surveillance system using enhanced edge localization

mechanism and gradient directional masking. This method

detects moving object with better accuracy than existed

methods relating to edge based methods. Because it works on

current successive frames and used an edge localization

method. And another advantage is it does not affected by

illumination changes. Pranab Kumar Dhar et al. compared

their error rate with Kim & Hwang and Dewan & Chae error

rate. Comparing the error rate results, the proposed method had less error.

Table1: Comparison of error rate (in percentage) among

the proposed and existing methods

Cases Kim &

Hwang

method

Dewan &

Chae

method

Proposed

method

Indoor

images

2.74

5.37

1.78

Outdoor

images

5.57

8.93 4.85

Foggy

images

21.51

34.03

15.52

Sunny

images

8.98

19.73

6.54

Nijad Al-Najdawi et al [4] proposed an Automated Real-Time

People Tracking System Based on KLT Features Detection.

They used Kanade-Lucas-Tomasi (KLT) technique to detect

features of both continuous and discontinuous nature of an

object. They proposed Kalman filter for object tracking. The

main advantage of this paper is low cast automatic object

tracking has proposed. Contrast analysis based algorithm is

proposed for object detection in real time for night time visual

surveillance [5]. The proposed method detect and tracking an

object very effectively at night time. In this [6], shape based

object detection has been done. They consider object

detection is a matching problem between image and model

segments. To rectify matching problem they find dominant

sets in the correspondence graph. Multiple objects can be

detected in an image when the proposed method gets all the dominant sets.

International Journal of Computer Applications (0975 – 8887)

Volume 91 – No.16, April 2014

39

Carlos Cuevas and Narciso García [7], proposed the

background modelling algorithm to detect real time moving

object. The proposed method combined the background model

and foreground model to detect an object from complex image

very quality taken by non-completely static cameras. It finds

the bandwidth matrices for the kernels which are used in

background modelling. This proposed method updating the

background model for reducing the misdetections. Bahadir

Karasulu and Serdar Korukoglu [8], proposed Moving object

detection and tracking by using annealed background

subtraction method in videos. Current frame is subtracted

from image and it is used to classify the pixel either

foreground or background by comparing the difference with

the threshold. Simulated annealing (SA) technique is used to

rectify the p – median problem. The total weighted distance

between demand points (nodes) and the closest facilities to

demand points are used to find the p number. SA-based

hybrid method is developed for performance optimization of

back, which is used to detect and track object(s) in videos.

Mandellos et al [9], is used to detect and tracking vehicles in

traffic surveillance using background subtraction. Scatter

background information carried in a series of frames, at pixel

level using Histogram-based filtering procedure, generating

reliable instances of the actual background. Background

instance was reconstructed on demand under any traffic

conditions. In this [10], moving objects are detected and

tracked using Adaptive background method. They used the

median filter for removing the noise in the objects taken from

the video sequence to get background image. One of the main

advantage of this proposed method is it runs quickly and

detect and tracking an object very effectively.

Ling Cai et al [11], proposed a stereo vision-based model for

multi-object detection and tracking in surveillance.

Illumination variation, shadow interference, and object

occlusion problems are overcome by using stereo model.

They identified the feature points after they projected into 2D

ground plane. For grouping the projected points according to

their height values and locations on the plane, a kernel-based

clustering algorithm is used. In [12], Bangjun Lei and Li-Qun

Xu proposed a detection and tracking of objects in wide range

of outdoor surveillance and monitoring scenarios in real-time

video analysis system. Adaptive background modelling

technique is used to extract the foreground regions. A blob

analysis is used for object tracking. It gives better result for

non-crowded and the static state of scene. Ian Fasel, Bret

Fortenberry and Javier Movellan [13], proposed to find

objects. Image generation and derive optimal inference

algorithms are used. It generates ratio models for object

versus background and it is learned using boosting methods.

The advantage of this method is very optimal for finding

objects. Christian Goerick, Detlev Noll and Martin Werne

[14], artificial neural network is used for real time car

detection and tracking. It reduced design cost and complexity.

Bijan Shoushtarian and Helmut E. Bez, [15], three different

dynamic background subtraction algorithms are used. Based

on the performance it is defined as Selective Updating using

Temporal Averaging, Non- foreground Pixels of the input

image based Selective Update and Selective Updating using

Temporal Median were only different for background pixels.

Among three, Selective Updating using Temporal Median

gives the better results than the rest of the two. The advantage

of this algorithm is dynamic reference image is created for

each input image. And another advantage is it gives better

result in uncontrolled indoor and outdoor scene.

Paolo Piccinini et al [16] detected and localizing the duplicate

objects in pick and place applications. SIFT keypoint

extraction and mean shift clustering is used to object model

and the image with real time. Euclidean transform is used to

delimiting points onto the current image. Gavrila, D.M. and

Philomin, V [17], presented a real time vehicle detection

based on shape based algorithm with Distance Transforms is

used to capture the different object shapes. Stochastic

optimization techniques (i.e. simulated annealing) used to

distribute shape in offline. Matching between the above

objects are detected. A boosted cascade of simple features

based algorithm is used to detect object showed in [18],

integral image is used to detect features. Selection of a small

number of critical visual features from a larger set and yields

extremely efficient classifiers with the help of Adaboost

learning algorithm. Complex classifiers arranged in cascade

for background regions of the image to be quickly discarded

while spending more computation on promising object. It runs

at 15 frames per second without resorting to image in real

time applications. Anuj Mohan et al [19], proposed an

example-based object detectors. To train human body four

distinct example based detectors are used. These four

detectors are trained for to detect the head, legs, left arm, and

right Arm after that these components are present in

configuration, second example-based classifier detects the

person or non-person. Component based approach and the

ACC data classification architecture are used to improve the

object detection. Jianxin Wu et al [20], proposed a C4: A

Real-time Framework for Object Detection. The speed of the

proposed method is 20 frames per second. This is one of the

main advantages. For capturing contour cues, comparison

taken among the neighboring pixels. CENTRIST visual

descriptor is used for contour based object detection.

3. REVIEW FOR OBJECT

RECOGNITION IN REAL TIME Woo-Han Yun, Sung Yang Bang and Daijin Kim, proposed

that objects are recognized with the help of relational

dependency among the objects represented by the graphical

model [21]. It is designed by transition matrix. Cascaded

adaboost detector is used for object detection. Using the

softmax function, object candidate is estimated by a logistic

regression. Christian Wohler [22] proposed pedestrian

recognition in real-time vision systems using Autonomous in

situ training of classification modules. Train the front and rear

views of pedestrians in dark clothes using classifier gives

better recognition capabilities. Supervised training algorithms

are applied when pedestrians wearing both dark and bright

clothes. Pedestrian recognition is done by situ training

process. Markus Ulrich, Carsten Steger and Albert

Baumgartner proposed modified Generalized Hough

Transform (GHT) for object recognition in realtime [23].

Improving the performance of GHT, some modification done

in GHT. Comparing the GHT with modified GHT, the

computation time and memory are less to modified GHT.

Nicoletta Noceti et al [24], presented on-line 3D object

recognition in videos using Spatio-temporal constraints.

Object modeling (training sequence) and the recognition

phase (test sequences) are constrained by local scale-invariant

features. Ad hoc matching procedure is used for recognition.

It has a view-based approach to 3D object recognition in

videos. The proposed method is based on the use of local

scale-invariant features and captures the essence of the object

appearance selecting those features that are stable in time

(i.e.,across view-point changes) and meaningful in space. A

compact description of the object, tolerant to scale,

International Journal of Computer Applications (0975 – 8887)

Volume 91 – No.16, April 2014

40

illumination, and view-point changes are produced by these

features

Lowe, D.G [25], objects are recognized with help of local

scale-invariant features. This is not affected by image scaling,

translation, and rotation, and it is partially invariant to

illumination changes and affine or 3D projection. These

properties of features used for object recognition. Features are

detected by filtering approach. It identifies stable points in

scale space. The image keys are created and used as input to a

nearest neighbor indexing method which identifies candidate

object matches. Lowe, D.G. [26] proposed 3D object

recognition using Local feature view clustering.

Matching of invariant local image features are used for Object

recognition. Combining multiple images of a 3D object into a

single model is presented for object recognition. 3D objects

are recognized from any viewpoint, imaging conditions

provide the non-rigid changes through generalization of

models, and improved robustness through the combination of

features. S. Belongie et al [27], presented object recognition

using shape matching by shape contexts. It finds the similarity

between shapes and it is used for object recognition.

Correspondences between points on the two shapes and

correspondences to estimate an aligning transform are used to

measure the similarity.

Mirmehdi, M. et al [28] presented a object recognition using

Feedback control strategies. It is used to find instances of a

generic class of objects by improving on established single-

pass hypothesis generation and verification approaches.

Optimal sets of low-level features are used to reduce the

number of hypotheses. The feedback control directs the search

for missing information using Top-down recognition. Hui

Chen and Bir Bhanu, [29] presented a Highly Similar 3D

Objects Recognition in Range Images using feature

embedding and SVM rank learning techniques. They

compared their methods with the Geometric Hashing (GH)

and the performance of the proposed gives better result than

the GH method.

Table:2 Comparison of the proposed approach with GH in

terms of the indexing performance and the search time(in

seconds) for the nearest neighbors

Dirk Walther et al [30], presented recognition of multiple

objects in cluttered scenes based on bottom-up visual

attention. Their result is compared with of David Lowe’s

recognition algorithm with and without attention. It gives

better result than David Lowe’s method. Manuele Bicego et al

[31], proposed appearance-based 3D object recognition using

A Hidden Markov Model approach. For obtaining overlapped

sub-images object is visited in a raster-scan fashion is used.

Wavelet coefficients are employed for sub image. HMM’s

used to model the sequence of vectors for initialization.

Classification is done by nearest neighbor rule and the

distance is measured by HMM. This method recognizes an

object very accurate in the case of heavily occluded or

distorted objects. Kirt Lillywhite et al [32] presented an object

recognition using Evolution-Constructed (ECO) features. It

creates datasets based only on a set of basic image transforms.

There is no need for a human expert to build feature sets or

tune their parameters and ability to generate specialized

feature sets for different objects and no limitations to certain

types of image sources. This is the major advantage of this

method. C. Wohler and J.K. Anlauf presented an adaptable

time delay approach neural network for real time object

recognition to complete image sequences at a time instead of

single images [33].Mahmoud Meribout, Mamoru Nakanishi

and Takeshi Ogura [34] presented a real-time object

recognition using parallel hardware architecture. A new

parallel HT algorithm is used for 2D object recognition in

unknown shape. CAM as a core processor used to support HT

algorithm. Young, S.S et al [35] proposed Object recognition

using multilayer Hopfield neural network. It consists of

several cascaded single-layer Hopfield networks.

Indexing Performance Time

ς(Fraction

of

Dataset)

SVM

rank

learning

GH

SVM

rank

learning

GH

0.5% (53.3%,

61.7%)

(1.65%,

32.7%)

(11.1,

1.06)

(9.6,

0.54)

5% (82.54%,

81.72%)

(23.59%,

57.68%)

10% (87.74%,

87.41%)

(35.61%,

69.93%)

15% (91.51%,

90.69%)

(45.99%,

76.49%)

20% (92.92%,

91.89%)

(53.77%,

81.55%)

25% (94.39%,

93.34%)

(59.67%,

85.88%)

30% (94.81%,

94.38%)

(64.39%,

89.57%)

35% (95.75%,

95.35%)

(69.33%,

91.42%)

International Journal of Computer Applications (0975 – 8887)

Volume 91 – No.16, April 2014

41

4. CONCLUSION Review of object detection, tracking and recognition is briefly

overviewed in this paper. The motive of this paper is who are

interested in this field for doing research work it will

definitely help to choosing the related algorithm for their

work accordingly.

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People Tracking System Based on KLT Features

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tracking system for outdoor night surveillance”, Pattern

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[7] Carlos Cuevas and Narciso García , “Improved

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parametric moving object detection strategies”, Image

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[8] Bahadir Karasulu and Serdar Korukoglu, “Moving object

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[10] Ruolin Zhang and Jian Ding, “Object Tracking and

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[29] Hui Chen and Bir Bhanu, “Efficient Recognition of

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AUTHOR’S INFORMATION 1Research Scholar, Department of Electronics and

Instrumentation, Bharathiar University, Coimbatore,Tamil

Nadu. India. 2Associate Professor Department of Electronics and

Instrumentation, Bharathiar University, Coimbatore,Tamil

Nadu. India. 3Research Scholar, Department of Electronics and

Instrumentation, Bharathiar University, Coimbatore,Tamil

Nadu. India.

C.Hemalatha received M.Sc and M.Phil degree in

Electronics and Instrumentation from Bharathiar University,

Coimbatore, Tamil Nadu, and India in 2010 and 2011

respectively. She is pursuing Ph. D (Electronics and

Instrumentation) in Bharathiar University. Her research

interests include Digital Image Processing, object recognition,

Neural Network and Signal Processing.

S.Muruganand received his M.Sc degree in Physics from

Madras University, Chennai, Tamil Nadu, India, and the Ph.D

degree from Bharathiar University in 2002. He is working as

an Assistant Professor in the Department of Electronics and

Instrumentation, Bharathiar University, Coimbatore, India.

His area of interest is Embedded Systems, Sensors, Physics

and Digital Signal Processing

R. Maheswaran received M.Sc degree in Electronics and

Instrumentation from Bharathiar University, Coimbatore,

Tamil Nadu, and India in 2010. He is pursuing Ph.D

(Electronics) in Bharathiar University. His research interests

include Embedded

System, LabVIEW, Digital Image Processing ,Wireless

Sensor Network, object recognition.

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